Experimental Personality Designs: Analyzing Categorical by Continuous Variable Interactions


  • Stephen G. West was partially supported by NIMH Grant P50-MH39246 and a sabbatical leave from Arizona State University to study at UCLA during the writing of this article. We thank Michael Kernis for making the data reported in Example 2 available to us. We thank the participants in the 1994 Nags Head Conference on Personality and Social Behavior for their suggestions. We also thank Khanh Bui, action editor William Chaplin, Eileen Donahue, David Funder, William Graziano, Gary McClelland, Julie Norem, and several anonymous reviewers for their helpful comments on an earlier version of the manuscript. Graphical help and suggestions from Patrick Curran, William Mason, Gary McClelland, and Robert Weiss are also acknowledged.

concerning this article should be addressed to Stephen G. West, Department of Psychology, Arizona State University, Box 87-1104, Tempe, AZ 85287-1104.


Theories hypothesizing interactions between a categorical and one or more continuous variables are common in personality research. Traditionally, such hypotheses have been tested using nonoptimal adaptations of analysis of variance (ANOVA). This article describes an alternative multiple regression-based approach that has greater power and protects against spurious conclusions concerning the impact of individual predictors on the outcome in the presence of interactions. We discuss the structuring of the regression equation, the selection of a coding system for the categorical variable, and the importance of centering the continuous variable. We present in detail the interpretation of the effects of both individual predictors and their interactions as a function of the coding system selected for the categorical variable. We illustrate two- and three-dimensional graphical displays of the results and present methods for conducting post hoc tests following a significant interaction. The application of multiple regression techniques is illustrated through the analysis of two data sets. We show how multiple regression can produce all of the information provided by traditional but less optimal ANOVA procedures.